Agent Training Method: Taking into account continuous action space of the agent we adopt the Deep Deterministic Policy Gradient (DDPG) [1]} to implement the training. DDPG maintains both actor \(\pi (s)\)  and critic \(Q(s, a)\)  networks, with parameters \(\theta _{\pi }\)  and \(\theta _{Q}\)  respectively. Policy \(\pi \)  is a mapping between a state \(s_t\)  and action \(a_t\) . \(Q(s_t, a_t)\)  is action-value function, it estimates expected reward after taking an action \(a_t\)  in state \(s_t\)  and then following greedy policy afterwards. It can be computed from recursive relationship known as Bellman equation:
\(Q(s_t, a_t) = r(s_t, a_t) + \gamma Q(s_{t+1}, \pi (s_{t+1}))\) 
